One Six has worked with an array of logistics companies over the years, and there are several common challenges that we hear when consulting with our logistics clients.

For those of you in the logistics industry, the below challenges probably seem all too familiar.

• Relying on multiple systems to operate your business (Order Management Systems, Warehouse Management Systems, Yard Management Systems, Transportation Management Systems, CRM, Invoicing/Finance Systems) that are not ideally integrated.
• Massive amounts of transactional data from the above systems that can get out of hand quickly (especially during peak shipping periods) and are rarely standardized across systems and operations.
• Large complex data sets, from lack of data governance, that make it difficult to streamline processes and automate operations.
• Operating on extremely thin margins while needing to navigate rising costs in fuel prices, labor costs, and other expenses.
• IT teams spending the majority of their time resolving customer issues versus proactively developing enhancements for the business.
• Increasing competition and mergers and acquisitions across the industry.

And without a modern solution in place, these challenges were resulting in inefficient operations that negatively affected their already tight margins, low customer satisfaction due to late, missed, or incorrect shipments, and decreased market share from competitors offering enhanced features and under-cutting them on price.

Fortunately, modern data solutions are available to address all of these challenges and avoid encountering the issues our logistics clients were dealing with. However, many logistics companies are still operating as legacy data organizations versus modern data organizations.

There are several key differences between modern and legacy data organizations, including the following:

Technology: Modern data organizations typically use advanced technologies such as cloud computing, big data platforms, and machine learning to collect, store, and analyze data. Legacy data organizations, on the other hand, may still be using older, less advanced technologies that are less capable of handling large amounts of data and providing insights.

Data sources: Modern data organizations typically collect data from a wide variety of sources, including internal systems, external sources, and IoT devices. Legacy data organizations may only collect data from a few sources, such as internal systems or customer interactions, which can limit their ability to gain insights.

Data management and analytics: Modern data organizations typically use advanced analytics and machine learning techniques to gain insights from their data, and they integrate these insights into their business processes and decision-making. Legacy data organizations may still rely on traditional methods such as manual analysis and reporting, which can be time-consuming and may not provide as much value. Additionally, legacy data organizations often keep their data siloed in separate systems across their organization, preventing a unified understanding of it.

How to start building a Modern Data Organization:

Start with a clear data strategy.

• The enterprise data strategy defines the organization’s data objectives and priorities and outlines a plan for implementing and managing data management and analysis processes. This ensures that the organization is using its data in a way that aligns with its business goals and objectives, and that the data is managed and analyzed in a consistent and effective manner.

The organization should then select and implement modern data platforms and tools that are suitable for their specific data needs.

• This involves choosing platforms and tools that are scalable, flexible, cost-effective, and easy to use. They should also offer a range of powerful features and capabilities for data processing, analysis, and visualization.

Once those tools are in place, the organization should establish modern data processes and teams that are well-versed in data management and analysis best practices.

• They should define clear roles and responsibilities for data management and analysis and provide training and support so that their teams are equipped to handle the organization’s data effectively.

The organization should then develop native-data applications that are designed to leverage the organization’s data in a practical and meaningful way. 

• This can involve building applications that help to improve decision-making, streamline processes, and drive business value, using the organization’s data as a key input.

Finally, the organization should invest in advanced analytics capabilities—such as machine learning and artificial intelligence—to help them uncover insights and patterns in their data that wouldn’t be possible with traditional methods. 

• These analytics capabilities improve decision-making, streamline processes and product innovation, and drive business value.

Making the transition to a modern data organization can be a challenge for logistics companies since managing and analyzing large amounts of data can be complex and time-consuming, especially since logistics companies typically have a diverse range of data sources and systems.

This is where One Six can help! We have extensive experience helping logistics companies navigate the complexities and simplify the path to data value.

And making the transition to a modern data organization can provide several benefits for logistics companies, including:

• Improved operational efficiency: By using data to optimize routes, delivery schedules, and inventory levels, logistics companies can improve their operational efficiency and reduce costs.

• Increased visibility: A modern data organization can provide logistics companies with real-time visibility into their operations, allowing them to quickly identify and respond to any issues or opportunities.

• Better decision-making: With access to real-time data and analytics, a modern data organization can provide logistics companies with actionable insights that can help them make better decisions.

• Predictive analytics: A modern data organization can use machine learning and predictive analytics to anticipate future trends and plan accordingly.

• Improved customer service: By using data to track and monitor shipments, a modern data organization can provide logistics companies with the ability to provide real-time updates on delivery status, resulting in improved customer service.

• Competitive advantage: A modern data organization can provide logistics companies with a competitive advantage by helping them to improve their operations, reduce costs, and make better decisions.

• Digital Transformation: A modern data organization also helps logistics companies to be more digital-savvy and stay ahead of the competition by leveraging new technologies and platforms.

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Get started transitioning to a Modern Data Organization

If you would like to see how we can help transition your business to a modern data organization, contact us today for a Free Consultation.